Juniper App: Technical Methodology

Overview: Data-Driven Analytics vs. Generative AI

Juniper App provides advanced neighborhood research tools powered by a deterministic data-aggregation engine. While our platform uses the .ai domain suffix — reflecting our modern technical architecture and high-performance data processing — we prioritize verifiable analytics over generative or autonomous decision-making.

• Deterministic logic: our neighborhood metrics and “Amenity Scores” are derived using fixed mathematical formulas applied to objective public datasets.
• No autonomous decisions: Juniper does not “rank” neighborhoods for individual suitability or make automated housing recommendations. Our tools are for market research only.

Data Sourcing & Transparency

Primary sources:

• Demographics & Housing: U.S. Census Bureau (ACS 2024, released Jan 2026).
• Rent Benchmarks: HUD Fair Market Rent (FMR) data (FY2026).
• Environmental Risks: FEMA National Risk Index (18 hazard types) and EPA Air Quality data.
• Amenity Data: OpenStreetMap contributors (ODbL).

For the full list of datasets, licenses, and attribution requirements, see our Data Sources & Attribution page.

Scoring Methodology

Composite scores (Dining, Recreation, Healthcare) are calculated using a weighted density approach:

• Objective inputs: every score is based on the count and proximity of verifiable amenities within a specific census tract.
• Fair Housing guardrails: scoring formulas exclude subjective “quality” rankings or socioeconomic proxies. Negative weights are strictly reserved for verified environmental hazards (e.g. FEMA flood risk), not demographic data.